Illustration for the Sazko Solutions article on the future of AI in software development
Posted on Author Mahesh Kumar Categories AI, DigitalTransformation, Software

The Future of AI in Software Development: What’s Actually Changing

By September 2026 the industry’s own framing had shifted: the value is in workflows, data rights and human review, not flashy demos. That’s a good moment to take stock of what has actually changed in how software gets built. It’s easy to be either dismissive or breathless about AI’s effect on software development, and both postures avoid the more useful question: what, specifically, is actually different about how software gets built today versus three years ago? A few concrete shifts are worth naming, separate from the marketing around them.

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Illustration for the Sazko Solutions article on n8n and AI-orchestrated workflow automation
Posted on Author Mahesh Kumar Categories AI, App Development, Software

n8n and the Rise of AI-Orchestrated Workflows

As of August 2026, several of the newest large models are being marketed specifically for managing long-running projects and collaborating with developers. Workflow orchestration tools are where a lot of that ambition meets reality. Workflow automation tools have existed for years, but something changed once AI models got good enough to sit inside the workflow rather than just around it. n8n is a useful example: an open-source, node-based automation tool that was already popular for connecting APIs and services, and has become a common substrate for AI-orchestrated workflows precisely because its workflows are structured, inspectable, and easy for both humans and models to reason about.

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Sazko Solutions article on AI-assisted formal verification for critical software, July 2026
Posted on Author Mahesh Kumar Categories AI, Security, Software

Formal Verification Meets AI: Why Machine-Checked Proofs Matter for Critical Systems

In July 2026, Mistral released a model aimed at formal software verification — generating machine-checked mathematical proofs, in Lean 4, that a piece of software behaves as specified. It’s a narrow capability with outsized implications for anyone building systems where “we tested it and it seemed fine” isn’t good enough.

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Illustration for the Sazko Solutions article on AI-assisted coding in brownfield versus greenfield software projects
Posted on Author Mahesh Kumar Categories AI, App Development, Software

AI-Assisted Coding: Brownfield vs. Greenfield Projects

July 2026 brought another wave of coding-focused model releases promising near-frontier capability at lower cost. They’re genuinely good — and they make the gap between greenfield and brownfield work more visible, not less. Most conversations about AI-assisted coding default to the easy case: a brand-new codebase, a clean slate, and a coding agent that scaffolds an app in minutes. That’s greenfield work, and it’s genuinely where tools like GitHub Copilot, Cursor, and Claude Code look most impressive. The harder and far more common reality for most engineering teams is brownfield: an existing system with years of undocumented decisions, tangled dependencies, and business logic nobody fully remembers the reason for.

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Sazko Solutions article on long-running AI agents that improve between sessions, mid-2026
Posted on Author Mahesh Kumar Categories AI, Software

Agents That Learn Between Sessions: The Move to Long-Running AI Workflows

In May 2026, Anthropic described a technique it called “dreaming” — letting an autonomous agent review its own past behaviour between sessions, spot patterns, and adjust how it works next time. It’s part of a broader industry push toward agents that handle long-running workflows in areas like coding, finance and legal work, rather than one-shot tasks. That shift changes what you have to build around an agent.

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